Privratsky-20210215 - New clustering
Hélène Fradin Kirshner
02/24/2021
rmarkdown::render(‘./2_New_clustering/2_New_clustering.Rmd’)
Changes in myeloid and kidney cells after CLP - Analysis of 2 x 10X scRNA-seq samples from 2 pools of WT mice (3 Sham + 3 CLP): comparison of gene expression in different cell populations
indir <- "./processedData/1_JP_analyses_results/Rerun_HARDAC_20210216/1_QC_filtering_metrics"
outdir <- "./processedData/2_New_clustering"
dir.create(outdir, recursive = T)library(Seurat)
filtered <- readRDS(paste0(indir, "/15.filtered.398.rds"))
filtered## An object of class Seurat
## 22399 features across 18055 samples within 1 assay
## Active assay: RNA (22399 features, 0 variable features)Normalize each sample individually and selected 2000 most variable genes between samples
library(cowplot)
list <- SplitObject(filtered, split.by = "sample.id")
list <- lapply(X = list, FUN = function(x) {
x <- NormalizeData(x)
x <- FindVariableFeatures(x, selection.method = "vst", nfeatures = 2000)
})
anchors <- FindIntegrationAnchors(object.list = list, dims = 1:20)
integrated <- IntegrateData(anchorset = anchors, dims = 1:20)
DefaultAssay(integrated) <- "integrated"
integrated <- ScaleData(integrated, verbose = T)
integrated## An object of class Seurat
## 24399 features across 18055 samples within 2 assays
## Active assay: integrated (2000 features, 2000 variable features)
## 1 other assay present: RNADefaultAssay(integrated) <- "integrated"
integrated <- RunPCA(integrated, npcs = 30, verbose = FALSE)integrated <- JackStraw(integrated, num.replicate = 100, dims = 30,
verbose = T)
saveRDS(integrated, paste0(outdir, "/1.integrated.56.rds"))
# integrated <-
# readRDS('./processedData/2_IL1R_KO_vs_ctrl/1.integrated.56.rds')integrated <- ScoreJackStraw(integrated, dims = 1:30)
j <- JackStrawPlot(integrated, dims = 1:30)
j
pdf(paste0(outdir, "/2_JackStrawPlot.pdf"), width = 10, height = 8)
j
dev.off()## png
## 2e <- ElbowPlot(integrated, ndims = 30)
e
pdf(paste0(outdir, "/3_ElbowPlot.pdf"))
e
dev.off()## png
## 2integrated <- RunUMAP(integrated, dims = 1:30)
integrated <- FindNeighbors(integrated, dims = 1:30)
# 0.4-1.2
for (i in seq(0, 2, 0.1)) {
integrated <- FindClusters(integrated, resolution = i, verbose = F)
}
head(integrated[[]])## orig.ident nCount_RNA nFeature_RNA percent.mito sample.id
## AAACCCAAGATGGCGT--C0 C0 572 394 10.839161 C0
## AAACCCAAGCAGTCTT--C0 C0 8811 2172 31.415276 C0
## AAACCCAAGCGAGGAG--C0 C0 12890 3208 21.807603 C0
## AAACCCAAGTAGGGTC--C0 C0 737 405 19.945726 C0
## AAACCCAAGTTTGTCG--C0 C0 634 364 21.451104 C0
## AAACCCACACTAACGT--C0 C0 1932 1151 5.124224 C0
## integrated_snn_res.0 seurat_clusters
## AAACCCAAGATGGCGT--C0 0 12
## AAACCCAAGCAGTCTT--C0 0 5
## AAACCCAAGCGAGGAG--C0 0 2
## AAACCCAAGTAGGGTC--C0 0 11
## AAACCCAAGTTTGTCG--C0 0 33
## AAACCCACACTAACGT--C0 0 1
## integrated_snn_res.0.1 integrated_snn_res.0.2
## AAACCCAAGATGGCGT--C0 2 3
## AAACCCAAGCAGTCTT--C0 0 0
## AAACCCAAGCGAGGAG--C0 0 0
## AAACCCAAGTAGGGTC--C0 1 2
## AAACCCAAGTTTGTCG--C0 4 5
## AAACCCACACTAACGT--C0 4 5
## integrated_snn_res.0.3 integrated_snn_res.0.4
## AAACCCAAGATGGCGT--C0 3 5
## AAACCCAAGCAGTCTT--C0 1 1
## AAACCCAAGCGAGGAG--C0 1 1
## AAACCCAAGTAGGGTC--C0 2 2
## AAACCCAAGTTTGTCG--C0 5 4
## AAACCCACACTAACGT--C0 5 4
## integrated_snn_res.0.5 integrated_snn_res.0.6
## AAACCCAAGATGGCGT--C0 6 4
## AAACCCAAGCAGTCTT--C0 1 1
## AAACCCAAGCGAGGAG--C0 0 0
## AAACCCAAGTAGGGTC--C0 2 2
## AAACCCAAGTTTGTCG--C0 4 8
## AAACCCACACTAACGT--C0 4 5
## integrated_snn_res.0.7 integrated_snn_res.0.8
## AAACCCAAGATGGCGT--C0 8 8
## AAACCCAAGCAGTCTT--C0 0 1
## AAACCCAAGCGAGGAG--C0 3 5
## AAACCCAAGTAGGGTC--C0 4 3
## AAACCCAAGTTTGTCG--C0 16 16
## AAACCCACACTAACGT--C0 6 6
## integrated_snn_res.0.9 integrated_snn_res.1
## AAACCCAAGATGGCGT--C0 7 9
## AAACCCAAGCAGTCTT--C0 0 0
## AAACCCAAGCGAGGAG--C0 1 3
## AAACCCAAGTAGGGTC--C0 3 8
## AAACCCAAGTTTGTCG--C0 13 14
## AAACCCACACTAACGT--C0 6 4
## integrated_snn_res.1.1 integrated_snn_res.1.2
## AAACCCAAGATGGCGT--C0 8 8
## AAACCCAAGCAGTCTT--C0 1 0
## AAACCCAAGCGAGGAG--C0 4 5
## AAACCCAAGTAGGGTC--C0 0 1
## AAACCCAAGTTTGTCG--C0 24 25
## AAACCCACACTAACGT--C0 3 4
## integrated_snn_res.1.3 integrated_snn_res.1.4
## AAACCCAAGATGGCGT--C0 7 8
## AAACCCAAGCAGTCTT--C0 2 0
## AAACCCAAGCGAGGAG--C0 3 7
## AAACCCAAGTAGGGTC--C0 11 1
## AAACCCAAGTTTGTCG--C0 12 25
## AAACCCACACTAACGT--C0 4 4
## integrated_snn_res.1.5 integrated_snn_res.1.6
## AAACCCAAGATGGCGT--C0 6 6
## AAACCCAAGCAGTCTT--C0 0 0
## AAACCCAAGCGAGGAG--C0 7 3
## AAACCCAAGTAGGGTC--C0 11 5
## AAACCCAAGTTTGTCG--C0 26 20
## AAACCCACACTAACGT--C0 4 7
## integrated_snn_res.1.7 integrated_snn_res.1.8
## AAACCCAAGATGGCGT--C0 15 16
## AAACCCAAGCAGTCTT--C0 0 0
## AAACCCAAGCGAGGAG--C0 6 3
## AAACCCAAGTAGGGTC--C0 10 9
## AAACCCAAGTTTGTCG--C0 26 21
## AAACCCACACTAACGT--C0 3 23
## integrated_snn_res.1.9 integrated_snn_res.2
## AAACCCAAGATGGCGT--C0 15 12
## AAACCCAAGCAGTCTT--C0 0 5
## AAACCCAAGCGAGGAG--C0 4 2
## AAACCCAAGTAGGGTC--C0 5 11
## AAACCCAAGTTTGTCG--C0 33 33
## AAACCCACACTAACGT--C0 3 1for (i in seq(0.2, 0.3, 0.01)) {
integrated <- FindClusters(integrated, resolution = i, verbose = F)
}# install.packages('clustree')
library(clustree)
c <- clustree(integrated, prefix = "integrated_snn_res.")
c
pdf(paste0(outdir, "/4_clustree.pdf"), width = 8.5, height = 11)
c
dev.off()## png
## 2# install.packages('clustree')
c <- clustree(integrated, prefix = "integrated_snn_res.", node_colour = "Il6",
node_colour_aggr = "mean")
c
pdf(paste0(outdir, "/5_clustree_Il6.pdf"), width = 8.5, height = 11)
c
dev.off()## png
## 2Idents(integrated) <- "integrated_snn_res.0.4"
table(integrated@active.ident)##
## 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15
## 3686 2917 2272 1665 1538 1099 641 531 499 464 443 432 406 345 318 207
## 16 17 18 19
## 189 176 144 83pal <- colorRampPalette(c("#12999E", "#FAEB09", "#E82564", "#03539C"))
levels <- levels(integrated$integrated_snn_res.0.4)
colors.clusters <- pal(length(levels))
names(colors.clusters) <- levels
colors.clusters## 0 1 2 3 4 5 6 7
## "#12999E" "#36A586" "#5BB26E" "#7FBF57" "#A4CC3F" "#C9D928" "#EDE610" "#F8D612"
## 8 9 10 11 12 13 14 15
## "#F5B620" "#F2972F" "#EF783D" "#EC594C" "#E9395A" "#DB2766" "#B72E6F" "#933578"
## 16 17 18 19
## "#6F3D81" "#4B448A" "#274B93" "#03539C"slices <- rep(1, length(levels))
pie(slices, col = colors.clusters, labels = names(colors.clusters))
d <- DimPlot(integrated, reduction = "umap", pt.size = 0.2, label = T,
label.size = 6, cols = colors.clusters)
d
pdf(paste0(outdir, "/6_DimPlot_umap_clusters_pc30_res0_4.pdf"),
width = 10, height = 8)
d
dev.off()## png
## 2colors.samples <- c("#12999E", "#FDA908")
names(colors.samples) <- levels(as.factor(integrated$sample.id))
slices <- rep(1, length(colors.samples))
pie(slices, col = colors.samples, labels = names(colors.samples))
p1 <- DimPlot(integrated, reduction = "umap", group.by = "sample.id",
pt.size = 0.2, cols = colors.samples)
p2 <- DimPlot(integrated, reduction = "umap", label = TRUE, pt.size = 0.2,
label.size = 6, cols = colors.clusters)
plot_grid(p1, p2)
pdf(paste0(outdir, "/7_2DimPlots_umap_samples_clusters_pc30_res0_4.pdf"),
width = 18, height = 8)
plot_grid(p1, p2)
dev.off()## png
## 2d <- DimPlot(integrated, reduction = "umap", group.by = "sample.id",
split.by = "sample.id", pt.size = 0.2, ncol = 2, cols = colors.samples)
d
pdf(paste0(outdir, "/8_DimPlot_umap_split_by_samples.pdf"), width = 16,
height = 9)
d
dev.off()## png
## 2f <- FeaturePlot(integrated, features = c("Nphs2", "Slc5a2",
"Clcnka", "Slc12a1", "Ptgs2", "Slc12a3", "Calb1", "Aqp2",
"Slc4a1", "Slc26a4", "Slc14a2", "Upk1a", "Cd22", "Adgre1",
"Pecam1", "Pdgfrb", "Cd68", "Cd14", "Acta2", "Csf3r", "Cd4"),
min.cutoff = "q9")
f
pdf(paste0(outdir, "/9_FeaturePlot_cellID.pdf"), width = 28,
height = 42)
f
dev.off()## png
## 2##Annotation of markers based on cluster markers from Susztak Science paper (Park et al., Science 360, 758–763 (2018) and Kidney International (2019) 95, 787–796; https://doi.org/10.1016/
https://science.sciencemag.org/content/360/6390/758.long https://www.kidney-international.org/article/S0085-2538(18)30912-8/fulltext
#Podocyte markers
f2 <- FeaturePlot(integrated, features = c("Nphs2", "Podxl"),
min.cutoff = "q9")
f2
pdf(paste0(outdir, "/10_FeaturePlot_Podo.pdf"), width = 14, height = 7)
f2
dev.off()## png
## 2Cluster 19 Clusters 4, 11, 16?
#Endothelial markers
f3 <- FeaturePlot(integrated, features = c("Plat", "Pecam1"),
min.cutoff = "q9")
f3
pdf(paste0(outdir, "/11_FeaturePlot_Endo.pdf"), width = 14, height = 7)
f3
dev.off()## png
## 2Cluster 11 Clusters 4, 16, 19?
#PT-S1 markers
f4 <- FeaturePlot(integrated, features = c("Slc5a2", "Slc5a12"),
min.cutoff = "q9")
f4
pdf(paste0(outdir, "/12_FeaturePlot_PTs1.pdf"), width = 14, height = 7)
f4
dev.off()## png
## 2#PT-S2 markers
f5 <- FeaturePlot(integrated, features = c("Fxyd2", "Hrsp12"),
min.cutoff = "q9")
f5
pdf(paste0(outdir, "/12_FeaturePlot_PTs2.pdf"), width = 10, height = 8)
f5
dev.off()## png
## 2#PT-S3 markers
f6 <- FeaturePlot(integrated, features = c("Atp11a", "Slc13a3"),
min.cutoff = "q9")
f6
pdf(paste0(outdir, "/13_FeaturePlot_PTs3.pdf"), width = 10, height = 8)
f6
dev.off()## png
## 2#Loop of Henle
f7 <- FeaturePlot(integrated, features = c("Slc12a1", "Umod"),
min.cutoff = "q9")
f7
pdf(paste0(outdir, "/14_FeaturePlot_LOH.pdf"), width = 10, height = 8)
f7
dev.off()## png
## 2#Distal CT
f8 <- FeaturePlot(integrated, features = c("Slc12a3", "Pvalb"),
min.cutoff = "q9")
f8
pdf(paste0(outdir, "/15_FeaturePlot_DCT.pdf"), width = 10, height = 8)
f8
dev.off()## png
## 2#Conn Tubule
f21 <- FeaturePlot(integrated, features = c("Calb1"), min.cutoff = "q9")
f21
pdf(paste0(outdir, "/29_FeaturePlot_ConnTub.pdf"), width = 10,
height = 8)
f21
dev.off()## png
## 2#CD PC
f9 <- FeaturePlot(integrated, features = c("Aqp2", "Hsd11b2"),
min.cutoff = "q9")
f9
pdf(paste0(outdir, "/16_FeaturePlot_CD-PC.pdf"), width = 10,
height = 8)
f9
dev.off()## png
## 2#CD-IC
f10 <- FeaturePlot(integrated, features = c("Atp6v1g3", "Atp6v0d2"),
min.cutoff = "q9")
f10
pdf(paste0(outdir, "/17_FeaturePlot_CD-IC.pdf"), width = 10,
height = 8)
f10
dev.off()## png
## 2#CD Trans
f11 <- FeaturePlot(integrated, features = c("Slc26a4", "Insrr",
"Rhbg"), min.cutoff = "q9")
f11
pdf(paste0(outdir, "/18_FeaturePlot_CD-Trans.pdf"), width = 10,
height = 8)
f11
dev.off()## png
## 2#Fibroblast
f12 <- FeaturePlot(integrated, features = c("Plac8", "S100a4",
"Pdgfrb"), min.cutoff = "q9")
f12
pdf(paste0(outdir, "/19_FeaturePlot_Fib.pdf"), width = 10, height = 8)
f12
dev.off()## png
## 2#Macro
f13 <- FeaturePlot(integrated, features = c("C1qa", "Cd68", "C1qb"),
min.cutoff = "q9")
f13
pdf(paste0(outdir, "/20_FeaturePlot_Macro.pdf"), width = 10,
height = 8)
f13
dev.off()## png
## 2#PMN
f14 <- FeaturePlot(integrated, features = c("S100a8", "Ly6g",
"S100a9"), min.cutoff = "q9")
f14
pdf(paste0(outdir, "/21_FeaturePlot_PMN.pdf"), width = 10, height = 8)
f14
dev.off()## png
## 2#B lymph
f15 <- FeaturePlot(integrated, features = c("Cd79a", "Cd79b",
"Cd19"), min.cutoff = "q9")
f15
pdf(paste0(outdir, "/22_FeaturePlot_Blymph.pdf"), width = 10,
height = 8)
f15
dev.off()## png
## 2#Tlymph
f16 <- FeaturePlot(integrated, features = c("Ltb", "Cd4", "Cxcr6"),
min.cutoff = "q9")
f16
pdf(paste0(outdir, "/23_FeaturePlot_Tlymph.pdf"), width = 10,
height = 8)
f16
dev.off()## png
## 2#NK
f17 <- FeaturePlot(integrated, features = c("Gzma", "Nkg7"),
min.cutoff = "q9")
f17
pdf(paste0(outdir, "/24_FeaturePlot_NK.pdf"), width = 10, height = 8)
f17
dev.off()## png
## 2#Novel1
f18 <- FeaturePlot(integrated, features = c("Slc27a2", "Lrp2",
"Cdca3"), min.cutoff = "q9")
f18
pdf(paste0(outdir, "/25_FeaturePlot_Novel1.pdf"), width = 10,
height = 8)
f18
dev.off()## png
## 2# library(Seurat)
DefaultAssay(integrated) <- "RNA"
clusters <- levels(integrated@active.ident)
conserved.markers <- data.frame(matrix(ncol = 14))
for (c in clusters) {
print(c)
markers.c <- FindConservedMarkers(integrated, ident.1 = c,
grouping.var = "sample.id", verbose = T)
markers.c <- cbind(data.frame(cluster = rep(c, dim(markers.c)[1]),
gene = rownames(markers.c)), markers.c)
write.table(markers.c, file = paste0(outdir, "/11_markers_",
c, ".txt"))
colnames(conserved.markers) <- colnames(markers.c)
conserved.markers <- rbind(conserved.markers, markers.c)
head(conserved.markers)
}## [1] "0"
## [1] "1"
## [1] "2"
## [1] "3"
## [1] "4"
## [1] "5"
## [1] "6"
## [1] "7"
## [1] "8"
## [1] "9"
## [1] "10"
## [1] "11"
## [1] "12"
## [1] "13"
## [1] "14"
## [1] "15"
## [1] "16"
## [1] "17"
## [1] "18"
## [1] "19"conserved.markers <- conserved.markers[-1, ]
write.table(conserved.markers, file = paste0(outdir, "/12_conserved.markers.tsv"),
quote = T, sep = "\t", col.names = NA)
saveRDS(conserved.markers, paste0(outdir, "/12.conserved.markers.rds"))
# conserved.markers <-
# readRDS('./2_MBP-clustering/12.conserved.markers.rds')integrated <- RenameIdents(integrated, `0` = "PT-s3", `1` = "PT-s1",
`2` = "PT-s2", `3` = "Endo", `4` = "CT", `5` = "PT-s3", `6` = "LOH",
`7` = "Fib", `8` = "DCT", `9` = "CD-PC", `10` = "Macro",
`11` = "CD-IC", `12` = "Lympho", `13` = "Novel", `14` = "Podo",
`15` = "PMN")
d2 <- DimPlot(integrated, label = TRUE, label.size = 8)
d2
pdf(paste0(outdir, "/32_Dimplot_newidents.pdf"))
d2
dev.off()## png
## 2d3 <- DimPlot(integrated, group.by = "sample.id", split.by = "sample.id",
pt.size = 0.2, ncol = 2)
d3
pdf(paste0(outdir, "/31_DimPlot_newidents_split_by_samples.pdf"),
width = 16, height = 9)
d3
dev.off()## png
## 2Identify cells expressing Il6
DefaultAssay(integrated) <- "RNA"
f19 <- FeaturePlot(integrated, features = "Il6", order = T, label = T,
label.size = 6)
f19
pdf(paste0(outdir, "/26_FeaturePlot_Il6.pdf"))
f19
dev.off()## png
## 2f20 <- FeaturePlot(integrated, features = c("Il6"), split.by = "sample.id",
max.cutoff = 3, cols = c("grey", "red"))
f20
pdf(paste0(outdir, "/27_FeaturePlot_Il6-sham-CLP.pdf"))
f20
dev.off()## png
## 2library(ggplot2)
library(cowplot)
theme_set(theme_cowplot())
integrated$celltype.stim <- paste(Idents(integrated), integrated$sample.id,
sep = "_")
integrated$celltype <- Idents(integrated)
Idents(integrated) <- "celltype"plots <- VlnPlot(integrated, features = c("Il6"), split.by = "sample.id",
group.by = "celltype", pt.size = 0, combine = FALSE)
library(patchwork)
wrap_plots(plots = plots, ncol = 1)
saveRDS(integrated, paste0(outdir, "/28.integrated.rds"))Session Information
sessionInfo()## R version 4.0.3 (2020-10-10)
## Platform: x86_64-conda-linux-gnu (64-bit)
## Running under: Red Hat Enterprise Linux Server release 6.8 (Santiago)
##
## Matrix products: default
## BLAS: /gpfs/fs1/data/omicscore/Privratsky-Privratsky-20210215/scripts/conda/envs/privratsky/lib/libblas.so.3.8.0
## LAPACK: /gpfs/fs1/data/omicscore/Privratsky-Privratsky-20210215/scripts/conda/envs/privratsky/lib/liblapack.so.3.8.0
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=en_US.UTF-8
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] patchwork_1.1.1 clustree_0.4.3 ggraph_2.0.4 ggplot2_3.3.3
## [5] cowplot_1.1.1 SeuratObject_4.0.0 Seurat_4.0.0
##
## loaded via a namespace (and not attached):
## [1] Rtsne_0.15 colorspace_2.0-0 deldir_0.2-9
## [4] ellipsis_0.3.1 ggridges_0.5.3 spatstat.data_2.0-0
## [7] leiden_0.3.7 listenv_0.8.0 farver_2.0.3
## [10] graphlayouts_0.7.1 ggrepel_0.9.1 codetools_0.2-18
## [13] splines_4.0.3 knitr_1.31 polyclip_1.10-0
## [16] jsonlite_1.7.2 ica_1.0-2 cluster_2.1.1
## [19] png_0.1-7 uwot_0.1.10 ggforce_0.3.2
## [22] shiny_1.6.0 sctransform_0.3.2 compiler_4.0.3
## [25] httr_1.4.2 Matrix_1.3-2 fastmap_1.1.0
## [28] lazyeval_0.2.2 limma_3.46.0 formatR_1.7
## [31] later_1.1.0.1 tweenr_1.0.1 htmltools_0.5.1.1
## [34] tools_4.0.3 igraph_1.2.6 gtable_0.3.0
## [37] glue_1.4.2 RANN_2.6.1 reshape2_1.4.4
## [40] dplyr_1.0.4 Rcpp_1.0.6 spatstat_1.64-1
## [43] scattermore_0.7 vctrs_0.3.6 nlme_3.1-152
## [46] gbRd_0.4-11 lmtest_0.9-38 xfun_0.20
## [49] stringr_1.4.0 rbibutils_2.0 globals_0.14.0
## [52] mime_0.10 miniUI_0.1.1.1 lifecycle_1.0.0
## [55] irlba_2.3.3 goftest_1.2-2 future_1.21.0
## [58] MASS_7.3-53.1 zoo_1.8-8 scales_1.1.1
## [61] tidygraph_1.2.0 promises_1.2.0.1 spatstat.utils_2.0-0
## [64] parallel_4.0.3 RColorBrewer_1.1-2 yaml_2.2.1
## [67] reticulate_1.18 pbapply_1.4-3 gridExtra_2.3
## [70] rpart_4.1-15 stringi_1.5.3 highr_0.8
## [73] Rdpack_2.1 rlang_0.4.10 pkgconfig_2.0.3
## [76] matrixStats_0.58.0 evaluate_0.14 lattice_0.20-41
## [79] ROCR_1.0-11 purrr_0.3.4 tensor_1.5
## [82] labeling_0.4.2 htmlwidgets_1.5.3 tidyselect_1.1.0
## [85] parallelly_1.23.0 RcppAnnoy_0.0.18 plyr_1.8.6
## [88] magrittr_2.0.1 R6_2.5.0 generics_0.1.0
## [91] pillar_1.4.7 withr_2.4.1 mgcv_1.8-33
## [94] fitdistrplus_1.1-3 survival_3.2-7 abind_1.4-5
## [97] tibble_3.0.6 future.apply_1.7.0 crayon_1.4.1
## [100] KernSmooth_2.23-18 plotly_4.9.3 rmarkdown_2.6
## [103] viridis_0.5.1 grid_4.0.3 data.table_1.13.6
## [106] metap_1.1 digest_0.6.27 xtable_1.8-4
## [109] tidyr_1.1.2 httpuv_1.5.5 munsell_0.5.0
## [112] viridisLite_0.3.0writeLines(capture.output(sessionInfo()), "./scripts/2_New_clustering/2_New_clustering.sessionInfo.txt")